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A hybrid EMG model for the estimation of multijoint movement in activities of daily living
Ding QC(丁其川); Zhao XG(赵新刚); Han JD(韩建达)
作者部门机器人学研究室
会议名称2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
会议日期September 28-30, 2014
会议地点Beijing, China
会议录名称Processing of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
出版者IEEE
出版地Piscataway, NJ, USA
2014
页码1-6
收录类别EI ; CPCI(ISTP)
EI收录号20150400443834
WOS记录号WOS:000358878700120
产权排序1
ISBN号978-1-4799-6731-5
关键词Surface Electromyography Human-robot Interface Motion Estimation Pattern Recognition
摘要Accurately identifying human's intent of motion from electromyography (EMG) signals is the key to implement EMG-based HRI (Human-Robot Interface) systems. Human's intent of motion includes motion modes and continuous movement variables. In this paper, a hybrid EMG-to-motion model is constructed by combining a classification model and a regression model. Based on a proper division for joints, the classification model is utilized to recognize the motion modes of 'small' joints; meanwhile, the regression model is utilized to estimate the continuous movement variables of 'big' joints. Furthermore, a Bayesian network (BN) model, which sufficiently employs context information of a task, is also involved into the hybrid model to improve its performances for motion estimation. Experiments have been conducted with three subjects to demonstrate the feasibility of the proposed methods. In these experiments, the motion modes of hand and wrist, and the continuous elbow angles are estimated with sEMG signals considering a 'drinking' task. Finally, an upper limb prosthetic is controlled to simulate human's movement in a 'drinking' task. © 2014 IEEE.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/15684
专题机器人学研究室
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, Liaoning, China
2.University of Chinese Academy of Sciences (UCAS), Beijing, China
推荐引用方式
GB/T 7714
Ding QC,Zhao XG,Han JD. A hybrid EMG model for the estimation of multijoint movement in activities of daily living[C]. Piscataway, NJ, USA:IEEE,2014:1-6.
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